In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update our best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.
It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g. Online learning algorithms may be prone to catastrophic interference.
This problem is tackled by incremental learning approaches.
Mini-batch techniques are used with repeated passing over the training data to obtain optimized out-of-core versions of machine learning algorithms, for e.g. When combined with backpropagation, this is currently the de facto training method for training artificial neural networks.
Depending on the type of model (statistical or adversarial), one can devise different notions of loss, which lead to different learning algorithms.These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.We would like to thank Mark Graus for programming the recommender systems used in EX1 and EX2, Steffen Rendle for implementing the explicit feedback MF algorithm, Niels Reijmer, Yunan Chen and Alfred Kobsa for their comments at several stages of this paper, and Dirk Bollen for allowing us to incorporate the results of his choice overload experiment (EX1) in this paper.The incremental gradient method can be shown to provide a minimizer to the empirical risk.Kernels can be used to extend the above algorithms to non-parametric models (or models where the parameters form an infinite dimensional space).